BITE (built-in test equipment) is widely used in many fields such as fault diagnosis, equipment prognosis and health management. The problems encountered in the process of BITE design and update, including the classifiers update, samples imbalance and hardware limitation, were analyzed, and the initial solutions were proposed. The density-based cluster and artificial immune system were applied to process the raw data; the delegates-based hybrid learning methods were proposed. The evaluation of the solution was validated by the numerical and experiment examples with support vector machine. Results show that the proposed solution can solve the mentioned problems well and is helpful for data based fault diagnosis design and update in the process of BITE maturation.